Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 20 of 119

1.
Pathog Dis. 2018 Sep 3. doi: 10.1093/femspd/fty071. [Epub ahead of print]

Comparative genomics of Czech vaccine strains of Bordetella pertussis.

Author information

1
Institute of Microbiology v.v.i., Laboratory of post-transcriptional control of gene expression, 14220 Prague, Czech Republic.
2
Laboratory for Biotechnology and Bioanalysis, Center for Reproductive Biology, Washington State University, Pullman, Washington 99164-7520.
3
University of Vienna, Institute for Theoretical Chemistry, Währinger Straße 17, A-1090 Vienna, Austria.
4
University of Vienna, Research group BCB, Faculty of Computer Science, Währinger Straße 24, 1090 Vienna, Austria.
5
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
6
Institute of Microbiology v.v.i, Laboratory of molecular biology of bacterial pathogens, 14220 Prague, Czech Republic.

Abstract

Bordetella pertussis is a strictly human pathogen causing the respiratory infectious disease called whooping cough or pertussis. B. pertussis adaptation to acellular pertussis vaccine pressure has been repeatedly highlighted, but recent data indicate that adaptation of circulating strains started already in the era of the whole cell pertussis vaccine (wP) use. We sequenced the genomes of five B. pertussis wP vaccine strains isolated in the former Czechoslovakia in the pre-wP (1954 - 1957) and early wP (1958 - 1965) eras, when only limited population travel into and out of the country was possible. Four isolates exhibit a similar genome organization and form a distinct phylogenetic cluster with a geographic signature. The fifth strain is rather distinct, both in genome organization and SNP-based phylogeny. Surprisingly, despite isolation of this strain before 1966, its closest sequenced relative appears to be a recent isolate from the US. On the genome content level, the five vaccine strains contained both new and already described regions of difference. One of the new regions contains duplicated genes potentially associated with transport across the membrane. The prevalence of this region in recent isolates indicates that its spread might be associated with selective advantage leading to increased strain fitness.

2.
Genes (Basel). 2018 Aug 1;9(8). pii: E392. doi: 10.3390/genes9080392.

RNA Structure Elements Conserved between Mouse and 59 Other Vertebrates.

Author information

1
Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria. thiel@tbi.univie.ac.at.
2
Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria. romanoch@tbi.univie.ac.at.
3
Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria. veerendra@tbi.univie.ac.at.
4
Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria. at@tbi.univie.ac.at.
5
Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria. ivo@tbi.univie.ac.at.
6
Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 29, 1090 Wien, Austria. ivo@tbi.univie.ac.at.

Abstract

In this work, we present a computational screen conducted for functional RNA structures, resulting in over 100,000 conserved RNA structure elements found in alignments of mouse (mm10) against 59 other vertebrates. We explicitly included masked repeat regions to explore the potential of transposable elements and low-complexity regions to give rise to regulatory RNA elements. In our analysis pipeline, we implemented a four-step procedure: (i) we screened genome-wide alignments for potential structure elements using RNAz-2, (ii) realigned and refined candidate loci with LocARNA-P, (iii) scored candidates again with RNAz-2 in structure alignment mode, and (iv) searched for additional homologous loci in mouse genome that were not covered by genome alignments. The 3'-untranslated regions (3'-UTRs) of protein-coding genes and small noncoding RNAs are enriched for structures, while coding sequences are depleted. Repeat-associated loci make up about 95% of the homologous loci identified and are, as expected, predominantly found in intronic and intergenic regions. Nevertheless, we report the structure elements enriched in specific genome elements, such as 3'-UTRs and long noncoding RNAs (lncRNAs). We provide full access to our results via a custom UCSC genome browser trackhub freely available on our website (http://rna.tbi.univie.ac.at/trackhubs/#RNAz).

KEYWORDS:

RNA regulation; RNA secondary structure; RNAz; conserved RNA structure elements; structural alignment

3.
Methods. 2018 Jul 1;143:70-76. doi: 10.1016/j.ymeth.2018.04.036. Epub 2018 May 4.

Efficient computation of co-transcriptional RNA-ligand interaction dynamics.

Author information

1
Department of Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria; Center for Anatomy and Cell Biology, Medical University of Vienna, Währingerstraße 13, 1090 Vienna, Austria. Electronic address: michael.wolfinger@univie.ac.at.
2
Department of Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria; Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria. Electronic address: xtof@tbi.univie.ac.at.
3
Department of Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria; Research Group BCB, Faculty of Computer Science, University of Vienna, Währingerstr. 29, 1090 Vienna, Austria; Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria. Electronic address: ivo@tbi.univie.ac.at.

Abstract

Riboswitches form an abundant class of cis-regulatory RNA elements that mediate gene expression by binding a small metabolite. For synthetic biology applications, they are becoming cheap and accessible systems for selectively triggering transcription or translation of downstream genes. Many riboswitches are kinetically controlled, hence knowledge of their co-transcriptional mechanisms is essential. We present here an efficient implementation for analyzing co-transcriptional RNA-ligand interaction dynamics. This approach allows for the first time to model concentration-dependent metabolite binding/unbinding kinetics. We exemplify this novel approach by means of the recently studied I-A 2'-deoxyguanosine (2'dG)-sensing riboswitch from Mesoplasma florum.

KEYWORDS:

Co-transcriptional folding; Energy landscape; RNA dynamics; RNA-ligand interaction; Riboswitch

4.
Methods. 2018 Jul 1;143:90-101. doi: 10.1016/j.ymeth.2018.04.003. Epub 2018 Apr 13.

In silico design of ligand triggered RNA switches.

Author information

1
Bioinformatics, Institute of Computer Science, and Interdisciplinary Center for Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107 Leipzig, Germany; University of Vienna, Faculty of Computer Science, Research Group Bioinformatics and Computational Biology, Währingerstraße 29, 1090 Vienna, Austria; University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstraße 17, 1090 Vienna, Austria. Electronic address: sven@bioinf.uni-leipzig.de.
2
Bioinformatics, Institute of Computer Science, and Interdisciplinary Center for Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107 Leipzig, Germany; University of Vienna, Faculty of Computer Science, Research Group Bioinformatics and Computational Biology, Währingerstraße 29, 1090 Vienna, Austria; University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstraße 17, 1090 Vienna, Austria.
3
University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstraße 17, 1090 Vienna, Austria; Medical University of Vienna, Center for Anatomy and Cell Biology, Währingerstraße 13, 1090 Vienna, Austria.
4
Bioinformatics, Institute of Computer Science, and Interdisciplinary Center for Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107 Leipzig, Germany.
5
University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstraße 17, 1090 Vienna, Austria.
6
University of Vienna, Faculty of Computer Science, Research Group Bioinformatics and Computational Biology, Währingerstraße 29, 1090 Vienna, Austria; University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstraße 17, 1090 Vienna, Austria.

Abstract

This contribution sketches a work flow to design an RNA switch that is able to adapt two structural conformations in a ligand-dependent way. A well characterized RNA aptamer, i.e., knowing its Kd and adaptive structural features, is an essential ingredient of the described design process. We exemplify the principles using the well-known theophylline aptamer throughout this work. The aptamer in its ligand-binding competent structure represents one structural conformation of the switch while an alternative fold that disrupts the binding-competent structure forms the other conformation. To keep it simple we do not incorporate any regulatory mechanism to control transcription or translation. We elucidate a commonly used design process by explicitly dissecting and explaining the necessary steps in detail. We developed a novel objective function which specifies the mechanistics of this simple, ligand-triggered riboswitch and describe an extensive in silico analysis pipeline to evaluate important kinetic properties of the designed sequences. This protocol and the developed software can be easily extended or adapted to fit novel design scenarios and thus can serve as a template for future needs.

KEYWORDS:

Inverse folding; Multi state design; Objective function; RNA design; RNA kinetics; Riboswitch

5.
Bioinformatics. 2018 Aug 1;34(15):2676-2678. doi: 10.1093/bioinformatics/bty158.

CMV: visualization for RNA and protein family models and their comparisons.

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
2
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.
3
Bioinformatics and Computational Biology Research Group, University of Vienna, Vienna, Austria.
4
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
5
Bioinformatics Group, Department of Computer Science, University of Leipzig, D-04107 Leipzig, Germany.
6
Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany.

Abstract

Summary:

A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest.

Availability and implementation:

Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS).

Supplementary information:

Supplementary data are available at Bioinformatics online.

6.
Methods Mol Biol. 2018;1704:363-400. doi: 10.1007/978-1-4939-7463-4_14.

Comparative RNA Genomics.

Backofen R1,2, Gorodkin J2, Hofacker IL2,3,4, Stadler PF5,6,7,8,9,10.

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.
2
Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria.
4
Bioinformatics and Computational Biology Research Group, University of Vienna, Währingerstraße 17, A-1090 Vienna, Austria.
5
Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark. studla@bioinf.uni-leipzig.de.
6
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. studla@bioinf.uni-leipzig.de.
7
Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
8
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
9
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
10
Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA. studla@bioinf.uni-leipzig.de.

Abstract

Over the last two decades it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible noncoding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of noncoding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

KEYWORDS:

Alternative splicing; Chromatin; Evolution; Long noncoding RNA; RNA secondary structure

7.
Bioinformatics. 2017 Sep 15;33(18):2850-2858. doi: 10.1093/bioinformatics/btx263.

RNAblueprint: flexible multiple target nucleic acid sequence design.

Author information

1
Faculty of Chemistry, Department of Theoretical Chemistry.
2
Faculty of Computer Science, Research Group Bioinformatics and Computational Biology.
3
Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria.
4
Center for Non-Coding RNA in Technology and Health, University of Copenhagen, Copenhagen DK-1870, Denmark.

Abstract

Motivation:

Realizing the value of synthetic biology in biotechnology and medicine requires the design of molecules with specialized functions. Due to its close structure to function relationship, and the availability of good structure prediction methods and energy models, RNA is perfectly suited to be synthetically engineered with predefined properties. However, currently available RNA design tools cannot be easily adapted to accommodate new design specifications. Furthermore, complicated sampling and optimization methods are often developed to suit a specific RNA design goal, adding to their inflexibility.

Results:

We developed a C ++  library implementing a graph coloring approach to stochastically sample sequences compatible with structural and sequence constraints from the typically very large solution space. The approach allows to specify and explore the solution space in a well defined way. Our library also guarantees uniform sampling, which makes optimization runs performant by not only avoiding re-evaluation of already found solutions, but also by raising the probability of finding better solutions for long optimization runs. We show that our software can be combined with any other software package to allow diverse RNA design applications. Scripting interfaces allow the easy adaption of existing code to accommodate new scenarios, making the whole design process very flexible. We implemented example design approaches written in Python to demonstrate these advantages.

Availability and implementation:

RNAblueprint , Python implementations and benchmark datasets are available at github: https://github.com/ViennaRNA .

Contact:

s.hammer@univie.ac.at, ivo@tbi.univie.ac.at or sven@tbi.univie.ac.at.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28449031
PMCID:
PMC5870862
DOI:
10.1093/bioinformatics/btx263
[Indexed for MEDLINE]
Free PMC Article
8.
J Am Chem Soc. 2017 Feb 22;139(7):2647-2656. doi: 10.1021/jacs.6b10429. Epub 2017 Feb 13.

NMR Structural Profiling of Transcriptional Intermediates Reveals Riboswitch Regulation by Metastable RNA Conformations.

Author information

1
Institute for Organic Chemisty and Chemical Biology, Center for Biomolecular Magnetic Resonance (BMRZ), Johann Wolfgang Goethe-Universität , Frankfurt/M. 60438, Germany.
2
Medical University of Vienna , Center for Anatomy and Cell Biology, Währingerstraße 13, 1090 Vienna, Austria.

Abstract

Gene repression induced by the formation of transcriptional terminators represents a prime example for the coupling of RNA synthesis, folding, and regulation. In this context, mapping the changes in available conformational space of transcription intermediates during RNA synthesis is important to understand riboswitch function. A majority of riboswitches, an important class of small metabolite-sensing regulatory RNAs, act as transcriptional regulators, but the dependence of ligand binding and the subsequent allosteric conformational switch on mRNA transcript length has not yet been investigated. We show a strict fine-tuning of binding and sequence-dependent alterations of conformational space by structural analysis of all relevant transcription intermediates at single-nucleotide resolution for the I-A type 2'dG-sensing riboswitch from Mesoplasma florum by NMR spectroscopy. Our results provide a general framework to dissect the coupling of synthesis and folding essential for riboswitch function, revealing the importance of metastable states for RNA-based gene regulation.

9.
Nucleic Acids Res. 2017 May 5;45(8):e60. doi: 10.1093/nar/gkw1325.

RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets.

Author information

1
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg C, Denmark.
2
Department of Veterinary Clinical and Animal Science, University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg C, Denmark.
3
Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark.
4
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria.
5
Bioinformatics Group, Department of Computer Science & IZBI-Interdisciplinary Center for Bioinformatics & LIFE-Leipzig Research Center for Civilization Diseases & Competence Center for Scalable Data Services and Solutions, University Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany.
6
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany.
7
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.
8
Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 17, 1090 Wien, Austria.

Abstract

Intermolecular interactions of ncRNAs are at the core of gene regulation events, and identifying the full map of these interactions bears crucial importance for ncRNA functional studies. It is known that RNA-RNA interactions are built up by complementary base pairings between interacting RNAs and high level of complementarity between two RNA sequences is a powerful predictor of such interactions. Here, we present RIsearch2, a large-scale RNA-RNA interaction prediction tool that enables quick localization of potential near-complementary RNA-RNA interactions between given query and target sequences. In contrast to previous heuristics which either search for exact matches while including G-U wobble pairs or employ simplified energy models, we present a novel approach using a single integrated seed-and-extend framework based on suffix arrays. RIsearch2 enables fast discovery of candidate RNA-RNA interactions on genome/transcriptome-wide scale. We furthermore present an siRNA off-target discovery pipeline that not only predicts the off-target transcripts but also computes the off-targeting potential of a given siRNA. This is achieved by combining genome-wide RIsearch2 predictions with target site accessibilities and transcript abundance estimates. We show that this pipeline accurately predicts siRNA off-target interactions and enables off-targeting potential comparisons between different siRNA designs. RIsearch2 and the siRNA off-target discovery pipeline are available as stand-alone software packages from http://rth.dk/resources/risearch.

PMID:
28108657
PMCID:
PMC5416843
DOI:
10.1093/nar/gkw1325
[Indexed for MEDLINE]
Free PMC Article
11.
Genome Biol. 2016 Oct 25;17(1):220.

Transcriptome-wide effects of inverted SINEs on gene expression and their impact on RNA polymerase II activity.

Author information

1
Department of Chromosome Biology, Max F. Perutz Laboratories, University of Vienna, Dr. Bohr Gasse 9/5, Vienna, A-1030, Austria.
2
Institute for Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna, A-1090, Austria.
3
Department of Cell and Developmental Biology, Medical University of Vienna, Schwarzspanierstrasse 17, Vienna, A-1090, Austria.
4
Present address: Center for molecular biology of the University Heidelberg, Im Neuenheimer Feld 282, Heidelberg, D-69120, Germany.
5
Department of Cell and Developmental Biology, Medical University of Vienna, Schwarzspanierstrasse 17, Vienna, A-1090, Austria. Michael.Jantsch@meduniwien.ac.at.
6
Department of Cell and Developmental Biology, Medical University of Vienna, Center of Anatomy and Cell Biology, Schwarzspanierstrasse 17, Vienna, A-1090, Austria. Michael.Jantsch@meduniwien.ac.at.

Abstract

BACKGROUND:

Short interspersed elements (SINEs) represent the most abundant group of non-long-terminal repeat transposable elements in mammalian genomes. In primates, Alu elements are the most prominent and homogenous representatives of SINEs. Due to their frequent insertion within or close to coding regions, SINEs have been suggested to play a crucial role during genome evolution. Moreover, Alu elements within mRNAs have also been reported to control gene expression at different levels.

RESULTS:

Here, we undertake a genome-wide analysis of insertion patterns of human Alus within transcribed portions of the genome. Multiple, nearby insertions of SINEs within one transcript are more abundant in tandem orientation than in inverted orientation. Indeed, analysis of transcriptome-wide expression levels of 15 ENCODE cell lines suggests a cis-repressive effect of inverted Alu elements on gene expression. Using reporter assays, we show that the negative effect of inverted SINEs on gene expression is independent of known sensors of double-stranded RNAs. Instead, transcriptional elongation seems impaired, leading to reduced mRNA levels.

CONCLUSIONS:

Our study suggests that there is a bias against multiple SINE insertions that can promote intramolecular base pairing within a transcript. Moreover, at a genome-wide level, mRNAs harboring inverted SINEs are less expressed than mRNAs harboring single or tandemly arranged SINEs. Finally, we demonstrate a novel mechanism by which inverted SINEs can impact on gene expression by interfering with RNA polymerase II.

KEYWORDS:

ADAR; Alu elements; Double-stranded RNA; Gene regulation; RNA Pol II; RNA editing; SINE; Transcription

PMID:
27782844
PMCID:
PMC5080714
DOI:
10.1186/s13059-016-1083-0
[Indexed for MEDLINE]
Free PMC Article
12.
Sci Rep. 2016 Oct 7;6:34589. doi: 10.1038/srep34589.

Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells.

Author information

1
RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
2
Institute of Virology, Philipps University Marburg, Hans-Meerwein-Str. 2, 35043 Marburg, Germany.
3
German Center for Infection Research (DZIF), partner site Gießen-Marburg-Langen, Hans-Meerwein Str. 2, 35043, Marburg, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
5
FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745 Jena, Germany.
6
Transcriptome Bioinformatics, Junior Research Group, Leipzig Research Center for Civilization Diseases, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
7
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
8
Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
9
Theoretical Biochemistry Group, Institute of Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria.
10
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110, Freiburg, Germany.
11
Research Group Theoretical Systems Biology, Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
12
Institute of Computer Science, Martin-Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle/Saale, Germany.
13
Department of Soil Ecology, UFZ - Helmholtz Centre for Environmental Research, Theodor-Lieser-Str. 4, 06120, Halle/Saale, Germany.
14
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany.
15
Biozentrum, University of Basel, Klingelbergstraße 50/70, CH-4056, Basel, Switzerland.
16
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
17
Junior Professorship for Computational EvoDevo, Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
18
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
19
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 54, 04109, Leipzig, Germany.
20
Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI), Systems Biology and Bioinformatics, Beutenbergstraße 11a, 07745, Jena, Germany.
21
Department of Bioanalytical Ecotoxicology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.
22
Doctoral School of Science and Technology, AZM Center for Biotechnology Research, Lebanese University, Tripoli, Lebanon.
23
TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany.
24
Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, Scotland, U.K.
25
Medical University of Vienna, Center for Anatomy and Cell Biology, Währingerstraße 13, 1090, Vienna, Austria.
26
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
27
Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 29, 1090, Vienna, Austria.
28
Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Brunswiker Str. 10, 24105, Kiel, Germany.

Abstract

The unprecedented outbreak of Ebola in West Africa resulted in over 28,000 cases and 11,000 deaths, underlining the need for a better understanding of the biology of this highly pathogenic virus to develop specific counter strategies. Two filoviruses, the Ebola and Marburg viruses, result in a severe and often fatal infection in humans. However, bats are natural hosts and survive filovirus infections without obvious symptoms. The molecular basis of this striking difference in the response to filovirus infections is not well understood. We report a systematic overview of differentially expressed genes, activity motifs and pathways in human and bat cells infected with the Ebola and Marburg viruses, and we demonstrate that the replication of filoviruses is more rapid in human cells than in bat cells. We also found that the most strongly regulated genes upon filovirus infection are chemokine ligands and transcription factors. We observed a strong induction of the JAK/STAT pathway, of several genes encoding inhibitors of MAP kinases (DUSP genes) and of PPP1R15A, which is involved in ER stress-induced cell death. We used comparative transcriptomics to provide a data resource that can be used to identify cellular responses that might allow bats to survive filovirus infections.

PMID:
27713552
PMCID:
PMC5054393
DOI:
10.1038/srep34589
[Indexed for MEDLINE]
Free PMC Article
13.
Cell Mol Life Sci. 2017 Feb;74(4):747-760. doi: 10.1007/s00018-016-2377-9. Epub 2016 Sep 27.

microRNA-122 target sites in the hepatitis C virus RNA NS5B coding region and 3' untranslated region: function in replication and influence of RNA secondary structure.

Author information

1
Institute of Biochemistry, Faculty of Medicine, Justus-Liebig-University, Friedrichstrasse 24, 35392, Giessen, Germany.
2
Faculty of Mathematics and Computer Science, Friedrich-Schiller-University, 07743, Jena, Germany.
3
Institute for Theoretical Chemistry, University of Vienna, 1090, Vienna, Austria.
4
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, 04107, Leipzig, Germany.
5
FLI Leibniz Institute for Age Research, 07743, Jena, Germany.
6
Institute of Biochemistry, Faculty of Medicine, Justus-Liebig-University, Friedrichstrasse 24, 35392, Giessen, Germany. michael.niepmann@biochemie.med.uni-giessen.de.

Abstract

We have analyzed the binding of the liver-specific microRNA-122 (miR-122) to three conserved target sites of hepatitis C virus (HCV) RNA, two in the non-structural protein 5B (NS5B) coding region and one in the 3' untranslated region (3'UTR). miR-122 binding efficiency strongly depends on target site accessibility under conditions when the range of flanking sequences available for the formation of local RNA secondary structures changes. Our results indicate that the particular sequence feature that contributes most to the correlation between target site accessibility and binding strength varies between different target sites. This suggests that the dynamics of miRNA/Ago2 binding not only depends on the target site itself but also on flanking sequence context to a considerable extent, in particular in a small viral genome in which strong selection constraints act on coding sequence and overlapping cis-signals and model the accessibility of cis-signals. In full-length genomes, single and combination mutations in the miR-122 target sites reveal that site 5B.2 is positively involved in regulating overall genome replication efficiency, whereas mutation of site 5B.3 showed a weaker effect. Mutation of the 3'UTR site and double or triple mutants showed no significant overall effect on genome replication, whereas in a translation reporter RNA, the 3'UTR target site inhibits translation directed by the HCV 5'UTR. Thus, the miR-122 target sites in the 3'-region of the HCV genome are involved in a complex interplay in regulating different steps of the HCV replication cycle.

KEYWORDS:

Accessibility; Ago2; Regulation; Translation; microRNA

PMID:
27677491
DOI:
10.1007/s00018-016-2377-9
[Indexed for MEDLINE]
14.
Nucleic Acids Res. 2016 Sep 30;44(17):8433-41. doi: 10.1093/nar/gkw558. Epub 2016 Jun 21.

RNAlien - Unsupervised RNA family model construction.

Author information

1
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria Bioinformatics Group, Department of Computer Science University of Freiburg, Georges-Köhler-Allee, 79110 Freiburg, Germany egg@informatik.uni-freiburg.de.
2
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria Bioinformatics Group, Department of Computer Science, University of Leipzig, D-04107 Leipzig, Germany Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany.

Abstract

Determining the function of a non-coding RNA requires costly and time-consuming wet-lab experiments. For this reason, computational methods which ascertain the homology of a sequence and thereby deduce functionality and family membership are often exploited. In this fashion, newly sequenced genomes can be annotated in a completely computational way. Covariance models are commonly used to assign novel RNA sequences to a known RNA family. However, to construct such models several examples of the family have to be already known. Moreover, model building is the work of experts who manually edit the necessary RNA alignment and consensus structure. Our method, RNAlien, starting from a single input sequence collects potential family member sequences by multiple iterations of homology search. RNA family models are fully automatically constructed for the found sequences. We have tested our method on a subset of the Rfam RNA family database. RNAlien models are a starting point to construct models of comparable sensitivity and specificity to manually curated ones from the Rfam database. RNAlien Tool and web server are available at http://rna.tbi.univie.ac.at/rnalien/.

PMID:
27330139
PMCID:
PMC5041467
DOI:
10.1093/nar/gkw558
[Indexed for MEDLINE]
Free PMC Article
15.
Mol Syst Biol. 2016 May 13;12(5):868. doi: 10.15252/msb.20156628.

Tristetraprolin binding site atlas in the macrophage transcriptome reveals a switch for inflammation resolution.

Author information

1
Max F. Perutz Laboratories, University of Vienna, Vienna, Austria.
2
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.
3
Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Vienna, Austria.
4
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark ivo.hofacker@univie.ac.at pavel.kovarik@univie.ac.at.
5
Max F. Perutz Laboratories, University of Vienna, Vienna, Austria ivo.hofacker@univie.ac.at pavel.kovarik@univie.ac.at.

Abstract

Precise regulation of mRNA decay is fundamental for robust yet not exaggerated inflammatory responses to pathogens. However, a global model integrating regulation and functional consequences of inflammation-associated mRNA decay remains to be established. Using time-resolved high-resolution RNA binding analysis of the mRNA-destabilizing protein tristetraprolin (TTP), an inflammation-limiting factor, we qualitatively and quantitatively characterize TTP binding positions in the transcriptome of immunostimulated macrophages. We identify pervasive destabilizing and non-destabilizing TTP binding, including a robust intronic binding, showing that TTP binding is not sufficient for mRNA destabilization. A low degree of flanking RNA structuredness distinguishes occupied from silent binding motifs. By functionally relating TTP binding sites to mRNA stability and levels, we identify a TTP-controlled switch for the transition from inflammatory into the resolution phase of the macrophage immune response. Mapping of binding positions of the mRNA-stabilizing protein HuR reveals little target and functional overlap with TTP, implying a limited co-regulation of inflammatory mRNA decay by these proteins. Our study establishes a functionally annotated and navigable transcriptome-wide atlas (http://ttp-atlas.univie.ac.at) of cis-acting elements controlling mRNA decay in inflammation.

KEYWORDS:

PAR‐CLIP; inflammation; mRNA decay; macrophage

PMID:
27178967
PMCID:
PMC4988506
[Indexed for MEDLINE]
Free PMC Article
16.
Algorithms Mol Biol. 2016 Apr 23;11:8. doi: 10.1186/s13015-016-0070-z. eCollection 2016.

RNA folding with hard and soft constraints.

Author information

1
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria.
2
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria ; Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria ; Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg, Denmark.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria ; Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg, Denmark ; Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany ; Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany ; Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1, 04103 Leipzig, Germany ; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM87501 USA.

Abstract

BACKGROUND:

A large class of RNA secondary structure prediction programs uses an elaborate energy model grounded in extensive thermodynamic measurements and exact dynamic programming algorithms. External experimental evidence can be in principle be incorporated by means of hard constraints that restrict the search space or by means of soft constraints that distort the energy model. In particular recent advances in coupling chemical and enzymatic probing with sequencing techniques but also comparative approaches provide an increasing amount of experimental data to be combined with secondary structure prediction.

RESULTS:

Responding to the increasing needs for a versatile and user-friendly inclusion of external evidence into diverse flavors of RNA secondary structure prediction tools we implemented a generic layer of constraint handling into the ViennaRNA Package. It makes explicit use of the conceptual separation of the "folding grammar" defining the search space and the actual energy evaluation, which allows constraints to be interleaved in a natural way between recursion steps and evaluation of the standard energy function.

CONCLUSIONS:

The extension of the ViennaRNA Package provides a generic way to include diverse types of constraints into RNA folding algorithms. The computational overhead incurred is negligible in practice. A wide variety of application scenarios can be accommodated by the new framework, including the incorporation of structure probing data, non-standard base pairs and chemical modifications, as well as structure-dependent ligand binding.

KEYWORDS:

Constraints; Dynamic programming; RNA folding

17.
Methods. 2016 Jul 1;103:86-98. doi: 10.1016/j.ymeth.2016.04.004. Epub 2016 Apr 5.

Predicting RNA secondary structures from sequence and probing data.

Author information

1
University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstrasse 17, 1090 Vienna, Austria. Electronic address: ronny@tbi.univie.ac.at.
2
University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstrasse 17, 1090 Vienna, Austria; Medical University of Vienna, Center for Anatomy and Cell Biology, Währingerstraße 13, 1090 Vienna, Austria. Electronic address: michael.wolfinger@univie.ac.at.
3
University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstrasse 17, 1090 Vienna, Austria. Electronic address: at@tbi.univie.ac.at.
4
University of Vienna, Faculty of Chemistry, Department of Theoretical Chemistry, Währingerstrasse 17, 1090 Vienna, Austria; University of Vienna, Faculty of Computer Science, Research Group Bioinformatics and Computational Biology, Währingerstr. 29, 1090 Vienna, Austria. Electronic address: ivo@tbi.univie.ac.at.

Abstract

RNA secondary structures have proven essential for understanding the regulatory functions performed by RNA such as microRNAs, bacterial small RNAs, or riboswitches. This success is in part due to the availability of efficient computational methods for predicting RNA secondary structures. Recent advances focus on dealing with the inherent uncertainty of prediction by considering the ensemble of possible structures rather than the single most stable one. Moreover, the advent of high-throughput structural probing has spurred the development of computational methods that incorporate such experimental data as auxiliary information.

KEYWORDS:

RNA secondary structure prediction; Structure probing

PMID:
27064083
DOI:
10.1016/j.ymeth.2016.04.004
[Indexed for MEDLINE]
Free full text
18.
Artif Life. 2016 Spring;22(2):172-84. doi: 10.1162/ARTL_a_00197. Epub 2016 Mar 2.

Computational Design of a Circular RNA with Prionlike Behavior.

Author information

1
University of Vienna.

Abstract

RNA molecules engineered to fold into predefined conformations have enabled the design of a multitude of functional RNA devices in the field of synthetic biology and nanotechnology. More complex designs require efficient computational methods, which need to consider not only equilibrium thermodynamics but also the kinetics of structure formation. Here we present a novel type of RNA design that mimics the behavior of prions, that is, sequences capable of interaction-triggered autocatalytic replication of conformations. Our design was computed with the ViennaRNA package and is based on circular RNA that embeds domains amenable to intermolecular kissing interactions.

KEYWORDS:

RNA structure; folding kinetics; self-replication; sequence design

19.
Nucleic Acids Res. 2016 Jan 4;44(D1):D90-5. doi: 10.1093/nar/gkv1238. Epub 2015 Nov 23.

AREsite2: an enhanced database for the comprehensive investigation of AU/GU/U-rich elements.

Author information

1
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17/3, A-1090 Vienna, Austria.
2
Max F. Perutz Laboratories, University of Vienna, Dr. Bohr-Gasse 9, A-1030 Vienna, Austria.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17/3, A-1090 Vienna, Austria at@tbi.univie.ac.at.
4
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17/3, A-1090 Vienna, Austria Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 29, A-1090 Vienna, Austria Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark.

Abstract

AREsite2 represents an update for AREsite, an on-line resource for the investigation of AU-rich elements (ARE) in human and mouse mRNA 3'UTR sequences. The new updated and enhanced version allows detailed investigation of AU, GU and U-rich elements (ARE, GRE, URE) in the transcriptome of Homo sapiens, Mus musculus, Danio rerio, Caenorhabditis elegans and Drosophila melanogaster. It contains information on genomic location, genic context, RNA secondary structure context and conservation of annotated motifs. Improvements include annotation of motifs not only in 3'UTRs but in the whole gene body including introns, additional genomes, and locally stable secondary structures from genome wide scans. Furthermore, we include data from CLIP-Seq experiments in order to highlight motifs with validated protein interaction. Additionally, we provide a REST interface for experienced users to interact with the database in a semi-automated manner. The database is publicly available at: http://rna.tbi.univie.ac.at/AREsite.

PMID:
26602692
PMCID:
PMC4702876
DOI:
10.1093/nar/gkv1238
[Indexed for MEDLINE]
Free PMC Article
20.
PLoS One. 2015 Oct 28;10(10):e0139900. doi: 10.1371/journal.pone.0139900. eCollection 2015.

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure.

Author information

1
Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark; Department of Veterinary Clinical and Animal Science, Faculty of Health and Medical Science, University of Copenhagen, Frederiksberg, Denmark.
2
Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio, United States of America.
3
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria; Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany; Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
4
Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.
5
Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark; Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria; Research Group Bioinformatics and Computational Biology, University of Vienna, Vienna, Austria.
6
Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark; Department of Cellular and Molecular Medicine, The Panum Institute, University of Copenhagen, Copenhagen, Denmark.

Abstract

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence.

PMID:
26509713
PMCID:
PMC4624896
DOI:
10.1371/journal.pone.0139900
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Loading ...
Support Center